Research And Application Of Deep Learning Recommendation Algorithm Based On ConvMF

Today,from the transition IT to DT era,the explosive growth of data,a large amount of information not only brings choice problems to people's lives and work,but many companies and enterprises also have difficulties in data governance.Therefore,in order to solve the problem of a large amount of data overload,researchers in this field have propose to recommend products or services that meet user preferences on the basis of user characteristics,but many systems still have cold start,sparse data,unclear potential features,and the single interaction and other issues have caused uneven recommendation accuracy.In order to solve the problems of sparse user rating data and single interaction mode with the system,aim for the scenario of intelligent movie on demand system,this paper innovatively combines intelligent voice interaction and web UI as the interactive interface to design a movie playlist recommendation system for satisfying the user's demand for intelligent selection of the order in this scenarioSpecifically,the main work done by the paper is as follows:(1)In order to explore the degree of users' preference for items and build the user's movie-viewing rating predictor,the shortcomings and optimization methods of CF based traditional scoring predictor are analyzed firstly,and the basic information feature matrix of users and movies are established based on this scenario studied in this paper,and the MFF-CF recommendation algorithm integrating multi-feature information is proposed.It is found that the accuracy of the scoring predictor obtained by this algorithm is better than traditional CF algorithm.(2)For furthering studying the influence of the introduction of the text information of the movie on the user's movie review score,the word vector model is constructed.Based on the Conv MF deep learning algorithm,the word vector model trained by the same area context is used as the input of the Text-CNN embedding layer.(3)In addition,considering the differentiation of users' initial scores,the PMF matrix decomposition process is also optimized.Finally,this paper proposed SD-Conv CMF recommendation algorithm based on deep learning,and MFF-CF is integrated with the deep learning recommendation algorithm.The experimental results show that the predictive effect of the improved recommendation algorithm is better.(4)To achieve a more interactive way on video single recommendation system,this paper design of voice and web interaction,building film information crawl,data storage module,data processing engine module,and the recommendation algorithm engine module,finally through the distributed deployment implemented a family oriented intelligent video on demand scenario movie recommendation system,and the system has been successfully applied to the intelligent home movie theater recommended speech robot,video on demand to provide intelligent choice for the user.The test shows that it can clearly reflect users' viewing preference and rating tendency.